US20120076403A1 - System and method for all-in-focus imaging from multiple images acquired with hand-held camera - Google Patents

System and method for all-in-focus imaging from multiple images acquired with hand-held camera Download PDF

Info

Publication number
US20120076403A1
US20120076403A1 US12/888,684 US88868410A US2012076403A1 US 20120076403 A1 US20120076403 A1 US 20120076403A1 US 88868410 A US88868410 A US 88868410A US 2012076403 A1 US2012076403 A1 US 2012076403A1
Authority
US
United States
Prior art keywords
image
laplacian pyramid
images
pixel
row
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US12/888,684
Other languages
English (en)
Inventor
Oscar Nestares
Jianping Zhou
Yoram Gat
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Intel Corp
Original Assignee
Intel Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Intel Corp filed Critical Intel Corp
Priority to US12/888,684 priority Critical patent/US20120076403A1/en
Assigned to INTEL CORPORATION reassignment INTEL CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GAT, YORAM, NESTARES, OSCAR, ZHOU, JIANPING
Priority to TW100133939A priority patent/TW201227599A/zh
Priority to JP2013529447A priority patent/JP2013542495A/ja
Priority to EP11827627.8A priority patent/EP2619726A2/en
Priority to PCT/US2011/053018 priority patent/WO2012040594A2/en
Priority to CN201180045857XA priority patent/CN103109304A/zh
Priority to KR1020137007231A priority patent/KR20130055664A/ko
Publication of US20120076403A1 publication Critical patent/US20120076403A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/21—Indexing scheme for image data processing or generation, in general involving computational photography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/70—Circuitry for compensating brightness variation in the scene
    • H04N23/743—Bracketing, i.e. taking a series of images with varying exposure conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/95—Computational photography systems, e.g. light-field imaging systems
    • H04N23/951—Computational photography systems, e.g. light-field imaging systems by using two or more images to influence resolution, frame rate or aspect ratio

Definitions

  • focus is typically achieved at a single depth.
  • the camera may focus on the object in the foreground (leaving the background blurry), or on the background (leaving the foreground object blurry).
  • FIG. 1 is a flow chart illustrating the overall processing of an embodiment.
  • FIG. 2 is a flow chart illustrating an alignment process, according to an embodiment.
  • FIG. 3 is a flow chart illustrating the estimation of Euler angles, according to an embodiment.
  • FIG. 4 is a flow chart illustrating the blending process, according to an embodiment.
  • FIG. 5 is a data flow diagram illustrating the construction of a Laplacian pyramid, according to an embodiment.
  • FIG. 6 is a flow chart illustrating the reduction process, according to an embodiment.
  • FIG. 7 a flow chart illustrating the expansion process, according to an embodiment.
  • FIG. 8 is a data flow diagram illustrating the Laplacian pyramid reconstruction process, according to an embodiment.
  • FIG. 9 is a block diagram illustrating a software or firmware implementation of an embodiment.
  • An image alignment process may be used, and the aligned images may be blended using a process that may be implemented using logic that has relatively limited performance capability.
  • the blending process may take a set of aligned input images and convert each image into a Laplacian pyramid (LP).
  • LP Laplacian pyramid
  • the LP for an image is a data structure that includes several processed versions of the image, each version being of a different size.
  • the set of aligned images may therefore be converted into a set of LPs.
  • the LPs may be combined into a composite LP, which then undergoes Laplacian pyramid reconstruction (LPR).
  • LPR Laplacian pyramid reconstruction
  • FIG. 1 Overall processing is illustrated in FIG. 1 , according to an embodiment.
  • two or more images may be aligned.
  • the aligned images may be blended. Embodiments of both 110 and 120 are described in greater detail below.
  • a Gaussian multi-resolution representation of the gray level representation of an input image may be calculated.
  • a representation may be viewed as a pyramid structure, wherein a first representation or pyramid layer may be a relatively coarse representation of the image, and each succeeding representation may be a finer representation of the image relative to the previous representation.
  • This multi-resolution representation of an image may allow for a coarse-to-fine estimation strategy.
  • this multi-resolution representation of the input image may be computed using a binomial B 2 filter (1 ⁇ 4, 1 ⁇ 2, 1 ⁇ 4) for purposes of computational efficiency.
  • the sequence 220 through 240 may be performed for each level of the pyramid, beginning at the coarsest level.
  • the process may be based on a gradient constraint, which assumes that the intensities between two images being aligned (or registered) are displaced on a pixel by pixel basis, while their intensity values are conserved.
  • the gradient constraint may be stated as
  • I image intensity
  • d displacement
  • ⁇ I(p) I 2 (p) ⁇ I 1 (p)
  • I 2 (p) and I 1 (p) are the image intensities at pixel p.
  • Each pixel in the image may contribute one constraint and, in general, two unknowns. However, it may be assumed that camera rotation jitter may be dominating the image motion over the camera translation so that the displacement between two images can be expressed as
  • x 1 is the location of pixel p in homogeneous image coordinates
  • x 2 Px 1
  • boldface P is a particular projective transform that depends on three parameters describing the 3D camera rotation and the two focal lengths of the images (assuming a simple diagonal camera calibration matrix):
  • R is the 3D rotation matrix corresponding to the camera rotation.
  • each iteration may begin by gathering constraints from a sampling of pixels from a first input image.
  • the locations from which the constraints are formed may be chosen using a rectangular sampling grid in the frame of reference of the first input image, according to an embodiment.
  • Given these pixels and their constraints, a vector ⁇ may be estimated for each pixel. The process for estimating these angles, according to an embodiment, will be discussed in greater detail below.
  • a rotation matrix R may be determined according to (3) above.
  • the projective transform P may be calculated according to (2) above. With each iteration, the transform P may be combined with the transform P that resulted from the previous iteration, or from the previous resolution level.
  • the displacement d(p) may be calculated as the estimated interframe camera rotation.
  • the input frame and its succeeding frame may be aligned according to the estimated camera rotation.
  • bilinear interpolation may be used to obtain the displaced intensity values of the succeeding image at the identified pixel locations.
  • the images may be pre-processed to equalize their mean and standard deviation prior to the alignment.
  • FIG. 3 illustrates the estimation of Euler angles ( 220 above) in greater detail.
  • a constraint of the form of equation (4) may be created for each sampled pixel at the given resolution level. This results in an equation for each sampled pixel.
  • the resulting set of equations represents an over-determined system of equations that are each linear in ⁇ .
  • this system of equations may be solved. In the illustrated embodiment, the system may be solved using an M-estimator with a Tukey function.
  • a Laplacian pyramid may be constructed for each aligned image and for each color channel or, alternatively, for the intensity and two color channels of an appropriate color components representation. This construction will be described in greater detail below.
  • a Laplacian pyramid of an input image is a set of images derived from the input image. The derivation of these images includes linear filtering of the input image, followed by iterative reduction and expansion of the filtered input image. The resulting set of images includes images of varying sizes, so that conceptually they may be collectively modeled as a pyramid.
  • the Laplacian pyramids of the input images may be used to construct a composite Laplacian pyramid.
  • the pixel's coefficient may be compared to that of the corresponding pixels in the other LPs.
  • the pixel having the largest absolute value for its coefficient may be saved and used in the corresponding position in the composite pyramid.
  • the composite pyramid may thus be constructed from these saved pixels.
  • Each pixel in the composite pyramid represents the pixel having the largest coefficient (in absolute value) of all the corresponding pixels at respective comparable locations in the set of LPs.
  • the composite pyramid undergoes Laplacian pyramid reconstruction to create the final blended image. This is discussed in greater detail below with respect to FIG. 8 .
  • FIG. 5 illustrates the construction of a Laplacian pyramid ( 410 of FIG. 4 ).
  • An input image 510 may be iteratively reduced by a reduction process 520 .
  • input image 510 may be reduced to form an image 511 , which may then be reduced to form an image 512 .
  • Image 512 may then be reduced to form image 513 .
  • reduction includes a filtering process and the elimination of certain pixels.
  • the example of FIG. 5 shows three reductions; in alternative embodiments, the number of reductions may be different. The chosen number of reductions may be decided at least in part by the desired size for the final reduced image (image 513 in this example).
  • the final reduced image 513 then undergoes an expansion process 530 .
  • the expansion process will be described in greater detail below, and includes the interleaving of all-zero representations of pixels into the image undergoing expansion, followed by a filtering process.
  • an all-zero representation of a pixel may be a binary pixel where the data is all zeros.
  • the output of the expansion of image 513 may then be subtracted from the predecessor image of the image undergoing expansion. At this point, the output of the expansion of image 513 may be subtracted from image 512 , which is the predecessor image of image 513 .
  • the result of this subtraction may be saved as difference image 542 , which represents part of the eventual Laplacian pyramid.
  • the predecessor image 512 also undergoes expansion 530 .
  • the output of this expansion may then be subtracted from the predecessor of image 512 , i.e., image 511 .
  • the result of this subtraction may be saved as difference image 541 .
  • Image 511 similarly undergoes expansion 530 ; the result may be subtracted from image 510 to create difference image 540 , which may likewise be saved.
  • the saved difference images 540 , 541 , and 542 collectively represent the Laplacian pyramid.
  • the number of expansions is necessarily equal to the number of reductions.
  • the illustrated example shows three expansions; other embodiments may use a different number.
  • the reduction process ( 520 of FIG. 5 ) is illustrated in FIG. 6 , according to an embodiment.
  • a linear filter may be applied.
  • the filter may use the mask
  • This mask is not often used to construct Laplacian pyramids because it is a coarse approximation of a Gaussian, but it may produce high quality results in this particular application at a lower cost than other of the most commonly used filters. For this reason this particular version of the Laplacian pyramid may be viewed as a simplified Laplacian pyramid.
  • pixels may be removed from the filtered image.
  • every other row may be discarded.
  • every other pixel may be removed from each of the remaining rows. The result is the reduced image.
  • rows of pixels may be interleaved between the existing rows of the image. These inserted pixels may be all-zero pixel representations.
  • all-zero pixel representations may be interleaved with the original pixels. In these rows, the result is that every other pixel is an all-zero pixel representation. Therefore, after completion of 710 and 720 , every other row will be made of all-zero pixel representations. In the other rows, every other pixel will be an all-zero pixel representation.
  • a linear filter may be applied.
  • the filter may use the same mask described in the reduction process for the same reasons discussed there
  • Laplacian pyramid reconstruction (LPR, reference 440 of FIG. 4 ) is illustrated in FIG. 8 , according to an embodiment.
  • the inputs are shown as images 811 - 814 , which are the constituents of the composite Laplacian pyramid.
  • the smallest image 814 may be input to an expansion process 830 .
  • the expansion 830 may be the same process as expansion 520 above.
  • the output of this expansion may then be added to the next largest input, image 813 .
  • the sum may then be expanded and added to the next largest image 812 .
  • the resulting sum may be expanded and added to the next largest image 811 .
  • the result is the final blended image 840 .
  • An additional operation may be applied before comparing the coefficients for each pixel in each image of the pyramid ( 420 of FIG. 4 ). This would consist of applying a linear filter to each of the pyramid images in absolute value. In some cases this might increase the quality of the blended image at the additional computation cost of applying the linear filter. In one embodiment this filter is a 5 ⁇ 5 box filter.
  • One or more features disclosed herein may be implemented in hardware, software, firmware, and combinations thereof, including discrete and integrated circuit logic, application specific integrated circuit (ASIC) logic, and microcontrollers, and may be implemented as part of a domain-specific integrated circuit package, or a combination of integrated circuit packages.
  • the term software, as used herein, refers to a computer program product including a non-transitory computer readable medium having computer program logic stored therein to cause a computer system to perform one or more features and/or combinations of features disclosed herein.
  • FIG. 9 illustrates a software or firmware embodiment of the processing described herein.
  • system 900 may include a processor 920 and may further include a body of memory 910 .
  • Memory 910 may include one or more computer readable media that may store computer program logic 940 .
  • Memory 910 may be implemented as a hard disk and drive, a removable media such as a compact disk, a read-only memory (ROM) or random access memory (RAM) device, for example, or some combination thereof.
  • Processor 920 and memory 910 may be in communication using any of several technologies known to one of ordinary skill in the art, such as a bus.
  • Computer program logic 940 contained in memory 910 may be read and executed by processor 920 .
  • One or more I/O ports and/or I/O devices, shown collectively as I/O 930 may also be connected to processor 920 and memory 910 .
  • Computer program logic 940 may include alignment logic 950 .
  • Logic 950 may be responsible for aligning images of a scene for subsequent blending.
  • Logic 950 may implementing the processing discussed above with respect to FIGS. 2 and 3 .
  • Computer program logic 940 may also include LP construction logic 960 .
  • This module may include logic for construction of a Laplacian pyramid based on an input image, as discussed above with respect to FIGS. 5-7 .
  • Computer program logic 940 may also include logic 970 for the construction of a composite Laplacian pyramid, as discussed above with respect to reference 430 of FIG. 4 .
  • Computer program logic 940 may also include Laplacian pyramid reconstruction logic 980 .
  • This module may include logic for the creation of a blended image as described above with respect to reference 440 of FIG. 4 and with respect to FIG. 8 .

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)
  • Studio Devices (AREA)
US12/888,684 2010-09-23 2010-09-23 System and method for all-in-focus imaging from multiple images acquired with hand-held camera Abandoned US20120076403A1 (en)

Priority Applications (7)

Application Number Priority Date Filing Date Title
US12/888,684 US20120076403A1 (en) 2010-09-23 2010-09-23 System and method for all-in-focus imaging from multiple images acquired with hand-held camera
TW100133939A TW201227599A (en) 2010-09-23 2011-09-21 System and method for all-in-focus imaging from multiple images acquired with hand-held camera
JP2013529447A JP2013542495A (ja) 2010-09-23 2011-09-23 手持ちカメラを使用して取得した複数の画像から焦点の合った画像を得るシステム及び方法
EP11827627.8A EP2619726A2 (en) 2010-09-23 2011-09-23 System and method for all-in-focus imaging from multiple images acquired with hand-held camera
PCT/US2011/053018 WO2012040594A2 (en) 2010-09-23 2011-09-23 System and method for all-in-focus imaging from multiple images acquired with hand-held camera
CN201180045857XA CN103109304A (zh) 2010-09-23 2011-09-23 从用手持拍摄装置采集的多个图像的全对焦成像的系统和方法
KR1020137007231A KR20130055664A (ko) 2010-09-23 2011-09-23 휴대용 카메라로 획득된 복수의 이미지로부터 모든 초점이 맞는 이미징을 수행하기 위한 시스템 및 방법

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US12/888,684 US20120076403A1 (en) 2010-09-23 2010-09-23 System and method for all-in-focus imaging from multiple images acquired with hand-held camera

Publications (1)

Publication Number Publication Date
US20120076403A1 true US20120076403A1 (en) 2012-03-29

Family

ID=45870721

Family Applications (1)

Application Number Title Priority Date Filing Date
US12/888,684 Abandoned US20120076403A1 (en) 2010-09-23 2010-09-23 System and method for all-in-focus imaging from multiple images acquired with hand-held camera

Country Status (7)

Country Link
US (1) US20120076403A1 (ko)
EP (1) EP2619726A2 (ko)
JP (1) JP2013542495A (ko)
KR (1) KR20130055664A (ko)
CN (1) CN103109304A (ko)
TW (1) TW201227599A (ko)
WO (1) WO2012040594A2 (ko)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014172484A1 (en) * 2013-04-16 2014-10-23 DotProduct LLC Handheld portable optical scanner and method of using
US20150348239A1 (en) * 2014-06-02 2015-12-03 Oscar Nestares Image refocusing for camera arrays
US9332243B2 (en) 2012-10-17 2016-05-03 DotProduct LLC Handheld portable optical scanner and method of using
US20170084006A1 (en) * 2015-09-17 2017-03-23 Michael Edwin Stewart Methods and Apparatus for Enhancing Optical Images and Parametric Databases
US10674135B2 (en) 2012-10-17 2020-06-02 DotProduct LLC Handheld portable optical scanner and method of using

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10078888B2 (en) 2016-01-15 2018-09-18 Fluke Corporation Through-focus image combination

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5325449A (en) * 1992-05-15 1994-06-28 David Sarnoff Research Center, Inc. Method for fusing images and apparatus therefor
US5629988A (en) * 1993-06-04 1997-05-13 David Sarnoff Research Center, Inc. System and method for electronic image stabilization
US6271847B1 (en) * 1998-09-25 2001-08-07 Microsoft Corporation Inverse texture mapping using weighted pyramid blending and view-dependent weight maps
US6434265B1 (en) * 1998-09-25 2002-08-13 Apple Computers, Inc. Aligning rectilinear images in 3D through projective registration and calibration
US6469710B1 (en) * 1998-09-25 2002-10-22 Microsoft Corporation Inverse texture mapping using weighted pyramid blending
US20120237137A1 (en) * 2008-12-15 2012-09-20 National Tsing Hua University (Taiwan) Optimal Multi-resolution Blending of Confocal Microscope Images

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6173087B1 (en) * 1996-11-13 2001-01-09 Sarnoff Corporation Multi-view image registration with application to mosaicing and lens distortion correction
US6359617B1 (en) * 1998-09-25 2002-03-19 Apple Computer, Inc. Blending arbitrary overlaying images into panoramas
JP4955616B2 (ja) * 2008-06-27 2012-06-20 富士フイルム株式会社 画像処理装置、画像処理方法及び画像処理プログラム
US20100194851A1 (en) * 2009-02-03 2010-08-05 Aricent Inc. Panorama image stitching

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5325449A (en) * 1992-05-15 1994-06-28 David Sarnoff Research Center, Inc. Method for fusing images and apparatus therefor
US5488674A (en) * 1992-05-15 1996-01-30 David Sarnoff Research Center, Inc. Method for fusing images and apparatus therefor
US5629988A (en) * 1993-06-04 1997-05-13 David Sarnoff Research Center, Inc. System and method for electronic image stabilization
US6271847B1 (en) * 1998-09-25 2001-08-07 Microsoft Corporation Inverse texture mapping using weighted pyramid blending and view-dependent weight maps
US6434265B1 (en) * 1998-09-25 2002-08-13 Apple Computers, Inc. Aligning rectilinear images in 3D through projective registration and calibration
US20020114536A1 (en) * 1998-09-25 2002-08-22 Yalin Xiong Aligning rectilinear images in 3D through projective registration and calibration
US6469710B1 (en) * 1998-09-25 2002-10-22 Microsoft Corporation Inverse texture mapping using weighted pyramid blending
US20030179923A1 (en) * 1998-09-25 2003-09-25 Yalin Xiong Aligning rectilinear images in 3D through projective registration and calibration
US20120237137A1 (en) * 2008-12-15 2012-09-20 National Tsing Hua University (Taiwan) Optimal Multi-resolution Blending of Confocal Microscope Images

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Adelson et al., "Pyramid methods in image processing", Nov/Dec 1984, RCA Engineer, 29-6, pages 33-41 *
Shum et al., "Systems and Experiment Paper: Construction of Panoramic Image Mosaics with Global and Local Alignment", 2000, International Journal of Computer Vision 36(2), 101-130 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9332243B2 (en) 2012-10-17 2016-05-03 DotProduct LLC Handheld portable optical scanner and method of using
US10448000B2 (en) 2012-10-17 2019-10-15 DotProduct LLC Handheld portable optical scanner and method of using
US10674135B2 (en) 2012-10-17 2020-06-02 DotProduct LLC Handheld portable optical scanner and method of using
WO2014172484A1 (en) * 2013-04-16 2014-10-23 DotProduct LLC Handheld portable optical scanner and method of using
US20150348239A1 (en) * 2014-06-02 2015-12-03 Oscar Nestares Image refocusing for camera arrays
US9712720B2 (en) * 2014-06-02 2017-07-18 Intel Corporation Image refocusing for camera arrays
US20170084006A1 (en) * 2015-09-17 2017-03-23 Michael Edwin Stewart Methods and Apparatus for Enhancing Optical Images and Parametric Databases
US10839487B2 (en) * 2015-09-17 2020-11-17 Michael Edwin Stewart Methods and apparatus for enhancing optical images and parametric databases
US20210027432A1 (en) * 2015-09-17 2021-01-28 Michael Edwin Stewart Methods and apparatus for enhancing optical images and parametric databases
US11967046B2 (en) * 2015-09-17 2024-04-23 Michael Edwin Stewart Methods and apparatus for enhancing optical images and parametric databases

Also Published As

Publication number Publication date
CN103109304A (zh) 2013-05-15
JP2013542495A (ja) 2013-11-21
WO2012040594A3 (en) 2012-05-10
KR20130055664A (ko) 2013-05-28
EP2619726A2 (en) 2013-07-31
WO2012040594A2 (en) 2012-03-29
TW201227599A (en) 2012-07-01

Similar Documents

Publication Publication Date Title
Zhang et al. Designing a practical degradation model for deep blind image super-resolution
Zhang et al. Residual dense network for image super-resolution
Faramarzi et al. Unified blind method for multi-image super-resolution and single/multi-image blur deconvolution
Farsiu et al. Video-to-video dynamic super-resolution for grayscale and color sequences
Ji et al. Robust wavelet-based super-resolution reconstruction: theory and algorithm
JP5294343B2 (ja) 画像位置合わせ処理装置、領域拡張処理装置及び画質改善処理装置
EP1950700A1 (en) System and method for reconstructing restored facial images from video
US20120076403A1 (en) System and method for all-in-focus imaging from multiple images acquired with hand-held camera
EP3067858B1 (en) Image noise reduction
DE102011103423A1 (de) System und Verfahren für 3D-Videostabilisierung durch Verschmelzen von Ausrichtungssensormesswerten und Bildanordnungsabschätzungen
Liu et al. A new polarization image demosaicking algorithm by exploiting inter-channel correlations with guided filtering
EP3067863B1 (en) Image noise reduction
Kim et al. Deep image demosaicing for submicron image sensors
Umer et al. Deep super-resolution network for single image super-resolution with realistic degradations
CN108122218A (zh) 基于颜色空间的图像融合方法与装置
CN112150363B (zh) 一种基于卷积神经网络的图像夜景处理方法及运行该方法的计算模块与可读存储介质
Vandewalle et al. Joint demosaicing and super-resolution imaging from a set of unregistered aliased images
Bätz et al. Multi-image super-resolution using a locally adaptive denoising-based refinement
CN111083359A (zh) 图像处理方法及其装置、电子设备和计算机可读存储介质
Zhang et al. Video superresolution reconstruction using iterative back projection with critical‐point filters based image matching
Wang et al. Efficient image deblurring via blockwise non-blind deconvolution algorithm
Azgin et al. A high performance alternating projections image demosaicing hardware
Gevrekci et al. Image acquisition modeling for super-resolution reconstruction
Briand Low memory image reconstruction algorithm from RAW images
Saito et al. Color shrinkage for color-image sparse coding and its applications

Legal Events

Date Code Title Description
AS Assignment

Owner name: INTEL CORPORATION, CALIFORNIA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:NESTARES, OSCAR;ZHOU, JIANPING;GAT, YORAM;REEL/FRAME:025313/0383

Effective date: 20101025

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION